code: def __init__(self, input_dim, hidden_dim, output_dim, args):
      super(GIN, self).__init__()
      self.num_layers = args.num_layers
  
      self.pre_mp = nn.Sequential(nn.Linear(input_dim, hidden_dim))
  
      self.convs = nn.ModuleList()
      self.nn1 = Sequential(
          Linear(hidden_dim, hidden_dim),
          ReLU(),
          Linear(hidden_dim, hidden_dim),
      )
      self.convs.append(pyg_nn.GINConv(self.nn1))
      for l in range(args.num_layers - 1):
          self.nnk = Sequential(
              Linear(hidden_dim, hidden_dim),
              ReLU(),
              Linear(hidden_dim, hidden_dim),
          )
          self.convs.append(pyg_nn.GINConv(self.nnk))
  
      self.post_mp = nn.Sequential(
          nn.Linear(hidden_dim, hidden_dim),
          ReLU(),
          nn.Linear(hidden_dim, output_dim),
      )
  
imports: import torch
  import argparse
  import skip_models
  import torch.nn
  import torch.nn.functional
  import torch_geometric.transforms
  import torch_geometric.nn
  import torch.optim
  from torch.utils.data import DataLoader
  from torch_geometric.datasets import TUDataset
  from sklearn.metrics import *
  from torch.nn import Sequential, Linear, ReLU
  from deepsnap.dataset import GraphDataset
  from deepsnap.batch import Batch
label_desc: Use image node_id 1 to create a new GIN object from inherited base classes: torch.nn with example: obj = GIN(input_dim, hidden_dim, output_dim, args)